S3: Quality control charts based on imprecise information

Gholamreza Hesamian

Payame Noor University, Iran

Abstract

The control chart is a graph used to study how a process changes over time. They help visualize variation, find and correct problems when they occur, predict expected ranges of outcomes and analyze patterns of process variation from special or common causes. Control charts have found extensive application in improving the quality of manufacturing processes during the last decades. However, quality elements of a control chart are often observed or defined imprecisely in many real-life applications. Since its introduction by Zadeh, fuzzy set theory has been developed and applied in several statistical contexts to deal with imprecision. 

The conventional control charts are constructed based on exact  data collected from a key quality characteristic. However, in practice, such data may fail to describe the nature of several applications, such as the surface roughness of components or the transmission speed of certain lights through a material. Besides, making decisions on whether those products are conforming or nonconforming or judging if a process is in control or out of control usually includes some extent of human subjectivity relating to decision-makers’ intelligence and perceptions.  Thus, in order to adapt to these imprecise information, the conventional control charts are necessarily extended to imprecise control charts.